context-compiler 0.9.0.dev0

Deterministic conversational state engine for LLM applications.

Context Compiler

Context Compiler is a deterministic conversational state authority for LLM applications. It handles canonical directive execution, semantic validation, deterministic clarify decisions, runtime semantic continuation boundaries, and structured authoritative state for the host.

What Context Compiler provides

Context Compiler gives hosts fixed state rules:

• handle canonical explicit state changes with deterministic rules
• clarification instead of silent overwrite for blocked/ambiguous changes
• preserve supported pending continuation when explicit confirmation is required
• export and import authoritative state for host-managed persistence
• produce structured authoritative state for downstream host decisions

The model generates responses. The compiler owns state. Human-facing normalization, malformed-input recovery, and intent drafting belong outside core.

How the compiler metaphor works

Like a compiler, it parses canonical directives, validates them, applies fixed rules, and produces a stable result the host can use. It treats important instructions as structured state instead of temporary prompt text. It is not source-code compilation, not a reasoning model, and not a natural-language repair layer.

10-Second Example

User sets a premise once:

User: set premise current project uses uv

Outcome: premise state includes "current project uses uv".

Later in the conversation:

User: how should I run the tests?

Your host sends the saved authoritative state with this later request, so the model answers in the context of the saved premise (current project uses uv) instead of relying on memory of earlier conversation text.

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Deterministic behavior (examples)

Context Compiler makes state-change rules explicit so behavior stays repeatable.

The architecture has three layers:

• syntax classification decides whether input is a canonical directive, invalid directive-shaped syntax, or ordinary passthrough
• semantic evaluation decides whether a canonical directive updates state, clarifies, or no-ops
• semantic continuation optionally preserves a deterministic blocked transition that needs explicit yes / no

Explicit directive

set premise concise replies

• Base model: silently accepts / rewrites
• Context Compiler: applies a repeatable state update

Single-directive grammar

use docker and prohibit peanuts

• Without an authority layer: host/model behavior varies
• Context Compiler: treats this as invalid directive-shaped syntax, keeps authoritative state unchanged, and does not create pending continuation

State-dependent operation

clear state
use podman instead of docker

• Without explicit state transition rules: behavior depends on host/model handling
• Context Compiler: applies the deterministic resulting transition when docker is absent and use podman is otherwise valid; other semantic conflicts may still clarify

Lifecycle enforcement

clear state
change premise to formal tone

• Without explicit transition checks: behavior depends on host/model handling
• Context Compiler: asks for clarification and keeps saved state unchanged

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Architecture

User Input
     │
     ▼
Context Compiler
     │
     ▼
Decision
     │
     ▼
Host Application
 ├─ clarify → ask user
 ├─ passthrough → call LLM
 └─ update → authoritative state mutated; host may call LLM with compiled state

The compiler never calls the LLM. Your app decides what to do with the returned Decision.

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Quickstart

Use Context Compiler in your host application first:

from context_compiler import (
    create_engine,
    get_clarify_prompt,
    is_clarify,
    is_update,
)

engine = create_engine()

user_input = "set premise current project uses uv"
decision = engine.step(user_input)

if is_clarify(decision):
    show_to_user(get_clarify_prompt(decision))
elif is_update(decision):
    messages = build_messages(engine.state, user_input)
    render(call_llm(messages))
else:
    render(call_llm(user_input))

This is the main integration path: your app owns the model call and uses the compiler as the authority layer for state transitions.

For runnable application-layer examples, see context-compiler-example-integrations. That companion repository shows enforcement points built on compiler state, including retrieval filtering, schema selection, tool gating, execution authorization, gateway middleware, runtime continuation handling, and prompt construction.

Does it Work?

Yes. The current demo suite in this repository contains 8 scored demos (01-05, 07, 08, 09) plus 1 informational demo (06).

The current published verification matrix combines 7 current model runs across hosted/frontier providers and local Ollama models. In those current runs, baseline passed 24 / 56, reinjected-state passed 40 / 56, and both compiler paths passed 56 / 56.

→ Current demo set and output modes Current and historical published results: docs/demos-results.md

Interactive Playground

Use the REPL to explore behavior, learn the directive grammar, and debug or test host-side state rules.

pip install context-compiler
context-compiler

Preload options load authoritative state:

• --initial-state-json / --initial-state-file load saved state (via exported state JSON).

REPL commands (controller layer, not engine directives):

• state shows current saved state.
• preview <input> runs deterministic dry-run without mutating live state.
• step <input> is an explicit alias of normal bare-input step behavior.

Bare REPL input behavior remains unchanged.

Machine-Readable CLI Usage

Use --json when you want one complete JSON object per processed input line for non-interactive usage.

context-compiler --json < input.txt

Preload options load authoritative state:

• --initial-state-json / --initial-state-file load saved state (via exported state JSON).

Installation

Requirements:

• Python 3.11+

Install:

pip install context-compiler

Packaging notes:

• Base install includes the core authority-layer engine and CLI.
• Example and demo source files are available in the repository and source distribution.
• To run the demos from this repository, clone the repo and install context-compiler[demos].
• The [demos] extra installs optional dependencies such as LiteLLM. It does not install demo source files into site-packages.

Development

uv sync --group dev
uv run pytest

Decision API

Each user message produces a Decision.

class Decision(TypedDict):
    kind: Literal["passthrough", "update", "clarify"]
    state: dict | None
    prompt_to_user: str | None

Meaning:

kind	host behavior
passthrough	forward user input to LLM
update	authoritative state mutated; host may call LLM with updated state
clarify	show prompt_to_user and do not call the LLM

For normal app code, prefer the exported decision helpers (is_clarify, is_update, is_passthrough, get_clarify_prompt, get_decision_state) instead of direct key traversal.

See docs/api-reference.md for the full public API reference.

Common API entry points:

• engine lifecycle: create_engine(...), engine.step(...), engine.state, engine.premise, engine.policies, engine.export_json(...), engine.import_json(...)
• decision helpers: is_clarify(...), is_update(...), is_passthrough(...), get_clarify_prompt(...), get_decision_state(...)
• state transport: engine.export_json(...), engine.import_json(...)
• controller APIs: preview(...), step(...), state_diff(...)

Controller API (Reusable Outside REPL)

• preview(engine, user_input) performs a deterministic dry run and restores live engine state afterward
• step(engine, user_input) returns a reusable result envelope around one engine turn
• state_diff(state_before, state_after) summarizes structural state changes

For examples and helper accessors such as get_step_decision(...), get_preview_state_after(...), preview_would_mutate(...), and diff_has_changes(...), see docs/api-reference.md.

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State Model

The state model holds explicit user commitments that the host can treat as authoritative in future turns.

• premise = authoritative context that changes how future answers should be interpreted

• use = affirmative selection or preference

• prohibit = explicit exclusion

• Premise is a single value that can be set or replaced

• Policies are per-item (use or prohibit)

• State changes only through explicit directives

• No inference or semantic reasoning

• Non-canonical input normalization is outside the core state contract

Identical input sequences always produce identical state.

For live engine-owned reads, use engine.premise and engine.policies. engine.policies returns a caller-owned copy.

engine.state remains the public snapshot/serialization boundary when host code needs the full authoritative state object.

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When to use premise

Use premise for persistent context that changes how all answers should be interpreted, especially when it:

• applies across many turns
• significantly changes what solutions are valid
• cannot be fully captured as simple use / prohibit policies

Examples:

• “Current medications: …”
• “Outdoor event; no seating available”
• “GDPR data handling requirements apply”
• “System is deployed across multiple regions”
• “Limited time available”

In these cases, the premise acts as an authoritative context anchor that the host supplies to the model on every turn.

Use policies instead when the constraint is explicit and enforceable:

• “prohibit foods that may cause GI upset”
• “use handheld foods”
• “prohibit storing personal data beyond immediate use”
• “prohibit introducing new external dependencies”
• “use single-step preparation methods”

Example domains

Hosts define what policy items and premise mean in context. Common patterns include:

• safety-oriented constraints (for example, prohibited materials or tools)
• authority/evidence constraints (for example, cite only approved sources)
• software workflow constraints (for example, require uv, prohibit npm)
• accessibility/environment constraints (for example, no audio-only outputs)

Context Compiler enforces explicit directive and state rules. Domain reasoning still belongs to the host and model workflow.

If a user says something non-canonical such as a near miss, alternate phrasing, or a failed replacement request that would need reinterpretation, that normalization is outside core and must happen before canonical directives reach the compiler.

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Persistence Contract

export_json() / import_json() are the current persistence boundary.

• They transport authoritative state only
• Hosts own any broader interaction or session workflow around that state
• Pending continuation, if supported by the active engine contract, remains a runtime semantic concept rather than a documented persisted artifact

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Directive Examples

Set and change premise:

User: set premise concise replies
User: change premise to concise bullet points

Per-item policies:

User: use docker
User: prohibit peanuts

Replacement:

User: use podman instead of docker

If docker is absent from saved state, that does not make the directive pending. The user's intended resulting state is still unambiguous, so the replacement follows the deterministic use podman transition when otherwise semantically valid.

Removal and reset:

User: remove policy peanuts
User: reset policies
User: clear state

Grammar invariant: one input may contain at most one canonical directive. Directive-shaped invalid input is outside the canonical language, and clarify is reserved for canonical directives that later fail semantic evaluation against authoritative state.

Pending continuation is a separate runtime layer. It may exist only after a canonical directive reaches a supported semantic clarify case. It never repairs malformed syntax or reinterprets non-canonical input as a directive. An absent source item in a canonical replacement directive is not, by itself, such a clarify case.

Examples:

Valid:
use docker
use podman instead of docker
clear state

Invalid:
use docker and prohibit peanuts
set premise vegetarian and use docker
clear state then set premise new project

Quote behavior follows the current grammar literally:

Passthrough:
"use docker and prohibit peanuts"

Invalid:
use "docker and prohibit peanuts"
set premise "use docker and prohibit peanuts"

Quotes do not create protected literal regions inside a recognized directive payload.

For the normative grammar, classification rules, and syntax-versus-semantics boundary, see DirectiveGrammarSpec.md.

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Examples

• examples — minimal usage patterns for the core authority layer
• demos — concrete scenarios showing how behavior differs with and without the compiler
• context-compiler-example-integrations — runnable application-layer enforcement examples built around compiler state

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FAQ

Isn't this just prompt reinjection? No. Prompt construction is one downstream use of authoritative state. Context Compiler is the authority layer that decides when state changes are allowed, when clarification is required, and how continuation state is restored. For runnable application-layer examples, see context-compiler-example-integrations.

Human-facing interpretation is a separate concern. If you want to recognize non-canonical phrasing, recover from malformed input, narrow user intent, or turn a failed replacement request into a different canonical directive, do that before calling core.

Why not just use a plain dict? A plain dict can hold state for prompt construction, schema selection, tool gating, and other host behavior.

Context Compiler solves the authority problem: who updates that state, under which rules, and what happens when instructions conflict.

User: use python_script
User: prohibit python_script

Without an authority layer, the application must invent conflict-resolution and continuation rules itself. Context Compiler applies deterministic state-transition rules and can return clarification instead of silently overwriting state.

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Advanced topics

Guarantees

• State changes only through explicit user directives or confirmation.
• Identical input sequences produce identical compiler state.
• Model responses never modify compiler state.
• Ambiguous directives trigger clarification instead of changing state.
• Syntax errors never create pending continuation.

Behavioral tests and Hypothesis-based property tests verify these invariants.

Multiple engines

• Multiple engines

For a full documentation map, see docs/README.md.

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Design Notes

These docs cover the design and milestone details:

• Design philosophy
• Architecture boundaries
• Project overview
• Directive grammar specification

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Conformance Fixtures

tests/fixtures/ defines the cross-language conformance tests. These fixtures serve as the behavioral contract for compiler semantics across implementations.

Development Process

Most of this project and related projects were implemented with Codex across many development sessions, including substantial implementation, refactoring, and cross-language porting work. ChatGPT was used separately for design discussion, review, and planning. Conformance harnesses and tests were used to verify behavioral consistency rather than treating model output as the correctness check.

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License

Apache-2.0.